A methodology is presented which couples expert systems to neural networks for the purpose of monitoring and diagnostics of large complex systems such as nuclear power plants. In order to provide timely, concise and task-specific information about the many aspects of the system's processes and to determine the state of the system based on the interpretation of potentially noisy data a model-referenced approach is utilized. In it a rule-based system performs the basic interpretation and processing of the data using heuristic reasoning. Having access to a set of pretrained neural networks that typify general classes of the system's state the expert system is able to perform diagnostic functions. It compares on-line data from the plant's sensors to the results of neural computations. This allows the diagnostic function to be performed with a speed comparable to that of the temporal evolution of the system. Hence corrective action can be taken by an operator. The set of pretrained neural networks provide implicitly a generalized model of the system which includes normal and off-normal states. Neural Networks are trained either by actual data or by data produced by a simulator. Coupling them to a rule-based system is a way of taking advantage of the best features of both. The speed and pattern recognition capabilities of the pretrained neural networks and the reasoning and interpretative power of symbolic computations. Information granules are used as the basic propositions that constitute the power plant's description in the knowledge-base of the expert system. They have the canonical form g = X is G is X, where X is a random variable described by a probability density function, G is a qualification or elastic constraint on the values of X; and X is a qualification of the proposition "X is G". This formulation encompasses two types of data: Data referring to observables or measured quantities from the system's sensors and calculated data coming from models of the plant such as from pretrained neural nets. Both the expert system and the neural networks are implemented in C and run in a VAX cluster.